Abstract

Healthcare AI models thrive on vast datasets, but privacy concerns, exacerbated by regulations like HIPAA and GDPR, hinder data sharing. Federated Learning (FL) offers a decentralized approach where models train collaboratively across institutions without exchanging raw data. This article investigates FL's role in privacy-preserving AI for healthcare, covering its architecture, applications, benefits, technical methodologies, challenges, and future trends. By aggregating model updates instead of data, FL enables secure development of diagnostic tools, predictive analytics, and personalized medicine. Case studies from oncology imaging and electronic health records illustrate FL's impact, reducing breach risks while maintaining performance. Ethical implications, such as bias mitigation and consent, are also discussed. FL stands as a cornerstone for trustworthy AI in sensitive healthcare domains, promoting innovation amid stringent privacy mandates.